جست‌وجوی استاد و پژوهشگر

• Rock and Geomechanics
• Applied Hydrogeology
• Nuclear Waste Repositories
• +۶ مورد دیگر

Mohammadreza Jalali serves as Professor and Chair of Engineering Geology and Hydrogeology at RWTH Aachen University's Faculty of Civil Engineering. His academic journey spans multiple prestigious institutions including ETH Zurich and the University of Waterloo, with extensive industry experience at Schlumberger and GEOMEC Engineering. His current research focuses on the intersection of geomechanics and hydrogeology with practical applications in energy and waste management systems. Dr. Jalali earned his Ph.D. in Earth & Environmental Sciences from the University of Waterloo (2013), following an M.Sc. in Petroleum Engineering (2007) and B.Sc. in Mining Engineering (2004), both from the University of Tehran. His educational background combines engineering fundamentals with earth sciences expertise, providing a strong foundation for his interdisciplinary research approach. His research interests center on Rock and Geomechanics, Applied Hydrogeology, and their applications to critical infrastructure challenges. Specializing in Thermo-hydro-mechanical coupled problems in rocks, he investigates Hydro-mechanical characterization of rocks, Hydraulic stimulation techniques, and Discrete fracture network modeling. His work has significant implications for Nuclear Waste Repositories and Deep Geothermal Energy systems, addressing some of the most pressing energy and environmental challenges of our time. Analysis of his recent publications reveals a strong focus on the mechanical behavior of clay-rich formations, particularly Opalinus Clay which is crucial for nuclear waste disposal. His work combines advanced experimental techniques with sophisticated numerical modeling to understand rock behavior under complex thermo-hydro-mechanical conditions. The research shows increasing emphasis on energy storage applications, particularly hydrogen storage in salt caverns, reflecting emerging energy transition needs. Dr. Jalali maintains active collaborations with major underground research laboratories including the Grimsel Test Site in Switzerland and the Bedretto Underground Laboratory. His work integrates field-scale experiments with laboratory investigations and numerical modeling, creating a comprehensive research framework that bridges theoretical understanding with practical engineering applications.

Souvik Dasمشاهده پروفایل

استادیار

• High-energy particle physics
• Quantum computing
• Neuromorphic artificial intelligence
• +۱ مورد دیگر

Souvik Das is an Assistant Professor in the Department of Aerospace, Physics and Space Sciences at Florida Institute of Technology (Florida Tech). A high-energy physicist, he has been a member of the CMS Collaboration at CERN’s Large Hadron Collider since 2006, contributing to detector instrumentation and analyses that culminated in the 2012 discovery of the Higgs boson. Education & Experience: PhD Advisor: Anders Ryd (mentioned in INSPIRE profile) Postdoc: University of Florida (2011–2017) Staff Scientist: Purdue University (2017–2023) Current: Assistant Professor, Florida Tech (2023–present) Research Interests: Dr. Das focuses on experimental high-energy particle physics, particularly on the properties of the Higgs boson, electroweak symmetry breaking, and searches for new physics beyond the Standard Model. He has pioneered instrumentation efforts for the next-generation CMS detector upgrade and maintains active interests in quantum computing architectures and neuromorphic artificial intelligence as applied to particle-physics data analysis. Research Highlights: His work spans precision measurements of the Higgs boson width, searches for exotic Higgs decays into light pseudoscalars, studies of heavy-flavor and multi-strange hadrons in heavy-ion collisions, and development of machine-learning techniques for background estimation. His publications reflect contributions to both the CMS and STAR collaborations, covering topics from dielectron production to hypertriton formation. Scientific Awards: No specific awards were mentioned in the provided text blocks. Teaching & Mentoring: While no explicit list of students was provided, Dr. Das expresses a strong passion for teaching physics at both undergraduate and graduate levels. Laboratories & Teams: He leads instrumentation R&D within the CMS Collaboration and is involved in the Florida Tech High-Energy Physics group located in F.W. Olin Physical Sciences 342.

Kielan Yarrowمشاهده پروفایل

عضو هیئت علمی

• Multisensory Perception
• Temporal Perception
• Attention
• +۴ مورد دیگر

Dr. Kielan Yarrow is a Reader in the Department of Psychology & Neuroscience at City St George's, University of London . He previously held positions at UCL across the Institute of Neurology and Institute of Cognitive Neuroscience , where he conducted postdoctoral research on motor-related temporal perception. His academic journey includes a PhD from UCL (2003) and MSc/BSc distinctions in psychology. Education : PhD (Temporal illusions), MSc Experimental Methods in Psychology (Distinction), BSc History & Psychology (First Class) Dr. Yarrow's research focuses on multisensory perception , particularly how temporal perception interacts with attention , decision-making , and motor control . His work explores phenomena like chronostasis , temporal recalibration , and agency attribution through transcranial magnetic stimulation (TMS) and EEG studies. Recent publications (2025-2023) emphasize sensorimotor recalibration , cross-modal timing , and neural mechanisms of decision-making . Collaborations with institutions like University of Queensland and Justus Liebig University highlight his international research network. Research students in his lab have investigated topics such as interval timing , deception in motor cortex , and affordance processing , with completed theses spanning autism spectrum disorders and motor planning .

Sven Schägeمشاهده پروفایل

استادیار

• Cryptography
• Digital Signatures
• Authenticated Key Exchange
• +۳ مورد دیگر

Sven Schäge is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research focuses on cryptographic protocols, particularly on developing robust theoretical frameworks for secure digital signatures and key exchange mechanisms with strong security and efficiency guarantees. Specializes in tight cryptographic reductions and realistic security models Works on authenticated key exchange protocols and post-quantum cryptography Part of the Coding Theory and Cryptology group Research Interests: Schäge investigates fundamental limitations in cryptographic tasks, using provable security paradigms and meta-reductions to establish theoretical boundaries. His work includes optimizing communication complexity in key exchange protocols (e.g., the TOPAS protocol) and analyzing algebraic signature schemes vulnerable to linear combination attacks. He emphasizes practical instantiations of cryptographic primitives without relying on random oracles. Grants: Funded by the German Federal Ministry of Education and Research (BMBF) for projects DigiSeal (16KIS0695) and Huawei Technologies Düsseldorf for vHSM. Also supported by the European Union's Horizon Europe program (101096435) for CONFIDENTIAL6G. Collaborations: Collaborated with leading cryptographers like Eike Kiltz, Tibor Jager, and Tanja Lange. Key contributions include the TOPAS protocol and evaluations of post-quantum cryptographic candidates.

• Scientific Computing
• Machine Learning
• High-Energy Physics
• +۳ مورد دیگر

Prof. Dr. Ralf Keidel is a faculty member at the Chair for Scientific Computing , Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU), since 2022. His academic career spans roles at Worms University of Applied Sciences , where he has served as Professor of Computer Science since 1993, Scientific Director of the Center for Technology and Transfer (ZTT) since 2000, and Head of the Technology Transfer Office since 1996. He is a key figure in international collaborations, including the ALICE Collaboration Board at CERN (2003–2023), Bergen pCT Collaboration (since 2018), and the MODE Collaboration (since 2022). His research merges Computer Science with High-Energy Physics , focusing on machine learning , secure heterogeneous distributed systems , and optimization of detectors in particle physics . Projects like SIVERT (2020–2024) aim to enhance Proton Computed Tomography (pCT) for clinical radiation therapy using AI-driven visualization and real-time reconstruction. He also contributes to serious games for financial education and mobile applications for musical training. Key publications highlight his work on differentiable programming for detector optimization, deep reinforcement learning in particle tracking, and convolutional neural networks for radiation topology analysis. His projects often involve interdisciplinary teams from institutions like the University of Bergen , DKFZ Heidelberg , and Western Norway University of Applied Sciences .

Duygu Bayram Karaمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Electrical Machines and Energy Conversion
• +۳ مورد دیگر

Duygu Bayram Kara is an active Associate Professor in the Department of Electrical Engineering at Istanbul Technical University's Faculty of Electrical and Electronics Engineering, where she has been a faculty member since 2016 after serving as a Research Assistant (2007-2015). Her career includes a brief industry role at Baytek Construction (2015-2016), but she remains a core academic staff member with office EEF 7311 and contact number +90-212-285-6956. She maintains active profiles on Scopus, Web of Science, Google Scholar, and ResearchGate with an ORCID (0000-0001-8184-8510). Education: PhD in Electrical Engineering from Istanbul Technical University (2009-2015) MSc in Electrical Engineering (with thesis) from Istanbul Technical University (2006-2009) BSc in Electrical Engineering from Istanbul Technical University (2001-2006) Research Interests: Her work integrates Machine Learning and Signal Processing with Electrical Machines and Energy Conversion to address critical challenges in electric motor reliability. She pioneers Kalman filter-based data fusion for aging monitoring and fault detection in motors, while simultaneously applying deep neural networks to characterize polymer nanocomposite films for electrical/optical applications. This dual focus bridges theoretical signal processing with practical energy systems and material science. Publication Trends: Her 23 Scopus-indexed outputs (2010-2025) demonstrate consistent innovation in motor diagnostics using wavelet transforms and neural networks, evolving toward sophisticated data fusion techniques. Recent work (2022-2025) heavily emphasizes deep learning for nanocomposite material analysis and Kalman filter applications in aging monitoring, with her 2020 nanocomposite conductivity paper accumulating 29 Scopus citations. The research shows increasing interdisciplinary collaboration and real-world validation in automotive and energy systems. Scientific Awards: No specific prizes, fellowships, or medals are documented in the provided materials. Professional recognition includes IEEE Senior Membership (2020-present) and standard IEEE Membership (2014-present), reflecting industry standing. Advising and Grants: She currently supervises one thesis in progress. As Principal Investigator, she secured two significant projects: Integrated Condition Assessment Using Kalman Filter-Based Data Fusion with Sensor Data from Different Environments and Recurrence Analysis-Assisted Aging Monitoring (TÜBİTAK, March 2024-May 2026) for advanced motor reliability Design and Development of Low Power Permanent Magnet Synchronous Motors in accordance with IE4 class (BAP, March 2018-October 2020) focusing on energy-efficient motor design Labs and Teams: Based at ITU's Ayazaga Campus, she operates within the Department of Electrical Engineering's research ecosystem, leveraging international collaborations (evidenced by global co-author networks) for projects involving electric motor diagnostics and nanocomposite analysis. Her work requires specialized facilities for motor testing, sensor data acquisition, and computational modeling, though specific lab names aren't disclosed in the text.

Andreas Nuessingمشاهده پروفایل

پژوهشگر

• Brain research
• EEG forward problem
• Brain stimulation (TDCS)
• +۲ مورد دیگر

Dr. Andreas Nuessing is a researcher at the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science at the University of Münster, Germany. His work focuses on numerical methods, particularly unfitted discontinuous Galerkin methods and algebraic multigrid techniques, applied to biomedical problems such as the EEG forward problem and brain stimulation (TDCS). Education: Dr. rer. nat. in Mathematics, supervised by Prof. Dr. Christian Engwer and Prof. Dr. rer. nat. Carsten Wolters. Research Areas include: Development of numerical methods for solving PDEs in brain research Efficient implementations of discontinuous Galerkin methods Applications in biomedical engineering and neuroscience Scientific Contributions : 2015: Best poster award at BACI2015 conference Key projects: Efficient solvers for DG discretizations of saddle point problems (2012–2013), German Academic Exchange Service collaborations

Joshua Juvrudمشاهده پروفایل

استادیار

• Developmental Psychology
• Game Design
• Virtual Reality
• +۳ مورد دیگر

Joshua Juvrud is an Assistant Professor at the Department of Game Design , Uppsala University , based at Campus Gotland in Visby, Sweden. His research bridges developmental psychology, game design, and virtual reality, with a focus on how interactive technologies influence human cognition and social behavior across cultures. Research Interests: Developmental Psychology: Investigating infant cognition, gaze following, and social learning. Game Design: Exploring how game mechanics affect performance, engagement, and learning. Virtual Reality: Studying avatar-based identity expression and social interaction in VR environments. Cross-Cultural Psychology: Examining how cultural contexts shape cognitive and emotional development. Publication Trends: His recent work (2022–2025) emphasizes the intersection of digital media and developmental outcomes, including studies on expertise in arcade games, cultural resilience in infant gaze behavior, and gender identity in virtual spaces. Earlier work focused on foundational aspects of infant attention, action prediction, and the role of caregiving in perceptual development. Contact: Email: joshua.juvrud@speldesign.uu.se Phone: +46 72-999 96 52 | +46 18-471 84 47 | +46 498-10 84 47 Visiting Address: Strandgatan 1b (Residenset), 621 57 VISBY

• Cosmic Ray Physics
• Neutrino Astronomy
• Particle and High Energy Physics
• +۳ مورد دیگر

Dennis Soldin is an Assistant Professor in the Department of Physics & Astronomy at the University of Utah, where he has been appointed since December 2023. His research focuses on cosmic rays and neutrino physics at the intersection of astroparticle and elementary particle physics. Soldin utilizes data from major international collaborations including the IceCube Neutrino Observatory, the Forward Physics Facility at CERN, and the Trinity Neutrino Observatory. Dr. rer. nat. (PhD) from University of Wuppertal, Germany (2011-2017) Diploma (MSc) from University of Wuppertal, Germany (2005-2011) Soldin's research spans cosmic ray physics, neutrino astronomy, and high-energy particle physics. He specializes in analyzing data from the IceCube Neutrino Observatory to understand high-energy particle processes in extensive air showers initiated when cosmic rays enter Earth's atmosphere. His work is crucial for revealing the origin and nature of high-energy cosmic rays and has profound implications for multi-messenger astrophysics. Soldin has made significant contributions to cosmic ray anisotropy studies, neutrino oscillation measurements, and the search for sterile neutrinos. He is also involved in developing next-generation neutrino observatories, including the Forward Physics Facility at CERN and the Trinity Collaboration's new neutrino observatory at Frisco Peak in southern Utah. Soldin's publications demonstrate a consistent focus on advancing our understanding of cosmic rays and neutrinos through innovative analysis techniques. His work often involves developing novel reconstruction methods using machine learning, improving sensitivity to rare events, and exploring connections between different cosmic messengers. Recent papers show increasing involvement in next-generation experimental facilities while maintaining strong contributions to the IceCube collaboration's scientific output. North America Top Cited Paper Award 2024 from IOP Publishing 1st Prize Most Inspiring Talk 2022 from Helmholtz Association Bruno Rossi Prize 2021 from American Astronomical Society As an educator, Soldin teaches courses including "Intro To Particle Physics" and "The Universe," and supervises both Master's and Ph.D. research students. His teaching activities demonstrate his commitment to training the next generation of physicists. Soldin is actively involved in outreach activities, including the Particle Physics Camp for West High School students, Teachers Outreach Events at the University of Utah, and the IceCube Masterclass. Soldin is a key member of several major research collaborations. He serves on the IceCube Collaboration Board and has previously chaired the Cosmic Ray Working Group (2018-2023). Since 2021, he has been involved in planning the Forward Physics Facility at CERN, serving on the Coordination Panel and as convener for the working group on light hadron production. In 2024, he joined the Trinity Collaboration to build a new neutrino observatory at Frisco Peak in southern Utah. His work bridges experimental particle physics with cosmic-ray astrophysics, positioning him at the forefront of multi-messenger astronomy research.

Nagi N. Mekhielمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Processing
• High Performance Memory Systems
• +۵ مورد دیگر

Nagi N. Mekhiel is a Professor in the Department of Electrical and Computer Engineering at Toronto Metropolitan University (formerly Ryerson University), where he teaches courses including Digital Systems, Microprocessors, Advanced Computer Architecture, and Parallel Computing. His office is located in the George Vari Engineering and Computing Centre at 245 Church St., Toronto. Dr. Mekhiel received his B.Sc. in Electrical Engineering from Assiut University, Egypt (1973), M.A.Sc. from University of Toronto (1981), and Ph.D. in Computer Engineering from McMaster University (1995). Prior to academia, he worked as a Biomedical Engineer at Toronto's Hospital for Sick Children (1981-1987), Senior Hardware Engineer at Definicon Systems Corporation (1987-1990), and Senior Member of Technical Staff at Yarc System Corporation (1996-1998). His research focuses on solving fundamental computer industry challenges, particularly the processor/memory speed gap and scalability of parallel processors. His work spans computer architecture, parallel processing, high-performance memory systems, VLSI, and performance evaluation. Mekhiel's research has been cited in patents by major tech companies including IBM, Google, Apple, Intel, Microsoft, and others. Analysis of his recent publications reveals a strong trajectory toward quantum computing applications, orbital data processing architectures, and memory system innovations for big data applications. His work consistently addresses the processor/memory speed gap through novel architectural solutions, with increasing emphasis on quantum information representation and parallel time computing models. Intel Xeon+FPGA system access via Intel's Hardware Accelerator Research Program Senior Member of IEEE Dr. Mekhiel has supervised numerous graduate students who have co-authored publications with him in areas including facial recognition systems, quantum computing implementations, and parallel processing architectures. His research has received industry support through patent licensing agreements with Intellectual Ventures US. He leads a research group focused on advanced computer architecture solutions, with current projects involving quantum bit encoding, reconfigurable memory systems, and orbital network architectures for scalable multi-core processing.

• Electromagnetic Modeling
• Multi-Physics Modeling
• Computational Electromagnetics
• +۳ مورد دیگر

Stefan Eijsvogel is a Postdoc researcher at the Electromagnetic and MultiPhysics Modeling and Computation (EMPMC) Lab within the Department of Electrical Engineering at Eindhoven University of Technology. He holds a Bachelor's and Master's degree in Electrical Engineering from the same institution, with his Master's thesis focusing on wireless power transfer for medical applications and nanosatellite clock synchronization. From 2019 to 2023, he completed his Ph.D., extending the spatial spectral Maxwell solver for soft x-ray wafer metrology applications. His research interests include Gabor frame expansions, parallel computing in multi-layered media, and noise-robust inverse-scattering algorithms. He is a core member of the Sub-wavelength resolution Lensless Imaging Algorithms and Tests project (2018–2024), contributing to advanced imaging techniques. His work bridges computational electromagnetics and optics, with applications in high-resolution metrology and scattering analysis. Notable contributions include developing efficient algorithms for Gabor coefficients and parallel 3D electromagnetic scattering methods. Stefan has published extensively in peer-reviewed journals and conferences, with recent works focusing on phaseless inverse scattering and parametrized volume integral equations. His research emphasizes both theoretical advancements and practical implementations in soft x-ray regimes.

Ralph Millerمشاهده پروفایل

استاد

• Elementary Information Processing
• Memory and Forgetting
• Pavlovian Conditioning
• +۳ مورد دیگر

Ralph Miller is a Distinguished Professor of Psychology at Binghamton University, specializing in elementary information processing in humans and animals. His research focuses on learning, memory, and decision-making, particularly through Pavlovian conditioning frameworks. He holds a PhD and MS in Psychology from Rutgers University, an MS in Physics from Rutgers, and a BS in Physics from MIT. His work emphasizes contextual modulation of learning, temporal coding hypotheses, and the role of retrieval processes in explaining behavioral phenomena. Education: Post-Doctoral Fellowship, University of Cambridge PhD, MS (Psychology), Rutgers University MS (Physics), Rutgers University BS (Physics), Massachusetts Institute of Technology Miller’s research investigates how retrieval rules (e.g., extended comparator hypothesis) simplify conditioning theories, as well as temporal relationships in learning and relapse prevention in exposure therapy. His lab collaborates internationally with teams in England and France, and he mentors postdoctoral fellows and undergraduates. He has authored over 280 papers, with >20,500 citations and an h-index of 71. His awards include NIH Fellowships, SUNY Distinguished Professorship, and international honors like Fulbright and Erskine Fellowships. He has served as editor-in-chief of leading journals and lectured globally. Research trends in his articles emphasize renewal effects, contingency learning, and the integration of temporal coding with clinical applications. Current work explores retrieval mechanisms and extinction protocols to improve therapies for anxiety disorders.

Meenakshi Anuragمشاهده پروفایل

استادیار

• Cancer multi-omics analysis
• Immune informatics
• Breast cancer
• +۵ مورد دیگر

Meenakshi Anurag is an Assistant Professor in the Department of Medicine at Baylor College of Medicine, Houston, TX, where she leads a research program focused on precision oncology through bioinformatics and multi-omics data science. Her work targets cancer heterogeneity, particularly in breast cancer subtypes including rare male breast cancer, to improve diagnosis, treatment selection, and survival outcomes. Education: PhD in Bioinformatics, Institute of Genomics & Integrative Biology, New Delhi, India (2014) Postdoctoral Fellowship, Baylor College of Medicine, Houston Her research centers on cancer multi-omics analysis , immune informatics , and breast cancer therapeutics. Using next-generation sequencing and proteomics, she investigates molecular drivers of therapeutic resistance, notably identifying DNA damage repair defects as endocrine therapy resistance mechanisms in ER+ breast cancer. Current work explores immune-checkpoint factors in treatment-refractory disease through integration of genomic, transcriptomic, and metabolomic data with clinical profiles. Analysis of her 2024-2025 publications reveals dominant themes in proteogenomics of breast cancer (ER+, HER2+, TNBC), bladder cancer, and lung adenocarcinoma. Key trends include biomarker discovery for therapy resistance, computational deconvolution of chemotherapy responses using patient-derived xenografts, and identification of novel molecular vulnerabilities like NF1 depletion and LIG1 deficiency. Her work emphasizes translating multi-omics insights into clinical applications for precision oncology. Dr. Anurag conducts research at the BCM-Jewish Institute for Research (Lab), Room 645D, Houston, TX 77030, with technical expertise spanning NGS analysis, cancer genomics, and immune-informatics.

Graeme Mansonمشاهده پروفایل

مدرس ارشد

• Structural Health Monitoring (SHM)
• Damage Identification
• Uncertainty Analysis
• +۶ مورد دیگر

Dr Graeme Manson is a Senior Lecturer in the Department of Mechanical Engineering at the University of Sheffield, where he also serves as Course Director for all MSc courses and Final Year BEng/MEng Tutor and Project Co-ordinator. He holds a BEng(Hons) from Heriot-Watt University (1992) and a PhD in Mechanical Engineering from the University of Manchester (1996). His research focuses on structural health monitoring (SHM), including damage identification strategies, uncertainty analysis, hybrid optimization, and nonlinear system dynamics. Notably, he received an EPSRC Advanced Research Fellowship (2002–2007). Research interests span advanced signal processing, robust feature selection, and machine learning applications in SHM. Key projects include developing algorithms for fatigue crack detection in aircraft structures, uncertainty propagation in nonlinear systems, and optimizing sensor networks using genetic algorithms. Manson has contributed to EPSRC-funded initiatives such as 'Uncertainty Propagation in Structures' (2006–2011) and 'Smart Sensing for Structural Health Monitoring' (funded by EPSRC). His teaching includes MEC319 (Sensors, Actuators and Controllers) and MEC415 (Condition Monitoring). Awards include the EPSRC Advanced Research Fellowship, and his work has been published widely in journals like Structural Health Monitoring , Journal of Sound and Vibration , and Materials . Manson collaborates across disciplines, integrating machine learning and statistical methods to enhance SHM reliability under operational and environmental variability.

Youngkun Chungمشاهده پروفایل

پژوهشگر

• Environmental Engineering
• Water Treatment Technologies
• Nanomaterials
• +۳ مورد دیگر

Youngkun Chung is a Postdoctoral Research Associate in the Department of Chemical and Biomolecular Engineering at Rice University, working under Professor Michael Wong in the Wong Group and the US NSF-funded NEWT Center. His research focuses on developing photocatalysts and integrating them with catalytic-ceramic membranes to advance green and sustainable chemistry, with applications in pollutant degradation (e.g., PFAS, PPCPs) and resource efficiency. Education: B.S. in Spatial Environment System Engineering (Handong Global University, South Korea, Magna cum Laude), M.S. and Ph.D. in Civil and Environmental Engineering (KAIST, South Korea). He received the Best Ph.D. Dissertation Prize and was a Korea NRF Graduate Research Fellow. Research interests include nanomaterials synthesis, membrane technologies, and environmental remediation. His work spans laboratory to pilot-scale water treatment systems, addressing challenges in micropollutant analysis (via LC/MS) and fouling mitigation. Key contributions include novel ceramic membrane designs, radionuclide adsorption methods, and case studies on wastewater reuse in industries like textiles. He collaborates on projects such as gamma-irradiation impact studies and flow-through electrochemical systems for heavy metal removal.